The alpha-synuclein gene, SNCA, is unequivocally known to have a key role in the pathogenesis and genetic etiology of Parkinson's disease (PD); however, this knowledge has yet to have a viable translational impact on PD patients. In addition to the known rare causal mutations, common variants related to PD risk have been more recently identified by genome-wide association studies (GWAS). Unlike the rare coding mutations, these variants show only modest effects and are unlikely to represent sufficient causal factors on their own, as they are found at a high frequency in the general population. This suggests that these SNCA risk variants require the presence of additional genetic or environmental factors to initiate the causal mechanism to disease. The abundance of available SNP genotyping created by recent GWAS provides the necessary resource to exhaustively examine potential interactions between SNCA and other genes. However, these types of interaction studies are both time- and computationally-intensive, as well as susceptible to inconclusive findings due to low power. A promising and novel strategy to avoid these pitfalls arises from evidence suggesting that complex diseases, such as PD, result from the disruption of multiple functionally-related genes, or molecular networks. Restricting analyses to genes with functional connectivity allows us to effectively limit the number of analyses, while still considering the most promising potential interactions. We propose to use a comprehensive weighted functional linkage network (FLN) to prioritize genes for our interaction study based on known physical or biochemical interactions or functional similarities. Using these known relationships, we will analyze 4 existing PD GWAS totaling 5396 cases and 8796 controls to identify interactions between PD risk SNPs (rs356220 and rs356198, two independently associated SNCA SNPs identified in recent PD meta-GWAS), and SNPs near genes with functional relationships to SNCA (N=104 autosomal genes from FLN). We will evaluate the effectiveness of the FLN method by testing for enrichment of findings compared to randomly selected sets of genes. We will also test the utility of applying different cutoffs or filters to te FLN to identify optimal uses for these data. Finally, we will complete a replication of the strongest interaction results by genotyping the 10 most strongly associated interacting SNPs in an independent sample of PD cases (N=1163) and controls (N= 974). The identification of SNPs, genes and pathways that interact with SNCA has immediate translational significance due to the potential as therapeutic targets to minimize the toxic effects of this gene in PD. Resolving why some individuals carrying SNCA risk alleles avoid PD provides a powerful approach to furthering our understanding of the underlying disease pathology in PD. This project has the potential to identify important genetic relationships, with immediately interpretable functional connections, which may be key to resolving the role of SNCA variants in idiopathic PD cases and ultimately in the development of therapeutic strategies for halting the disease process.